ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables

06/26/2015
by   Jayanta Basak, et al.
0

Stochastic optimization is an important task in many optimization problems where the tasks are not expressible as convex optimization problems. In the case of non-convex optimization problems, various different stochastic algorithms like simulated annealing, evolutionary algorithms, and tabu search are available. Most of these algorithms require user-defined parameters specific to the problem in order to find out the optimal solution. Moreover, in many situations, iterative fine-tunings are required for the user-defined parameters, and therefore these algorithms cannot adapt if the search space and the optima changes over time. In this paper we propose an adaptive parameter-free stochastic optimization technique for continuous random variables called ASOC.

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